🧪 COVID-19 Global Health Analysis with Python

This repository contains an end-to-end analysis of global COVID-19 trends using Python and Jupyter Notebook. The project covers data cleaning, aggregation, and a variety of visualizations to highlight patterns in confirmed, death, and recovered cases across countries.

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📂 Files Included

• covid19_global_health_analysis.ipynb
  Main Jupyter notebook with all analysis and commentary.

• time_series_covid19_confirmed_global.csv
  Global confirmed cases dataset.

• time_series_covid19_deaths_global.csv
  Global deaths dataset.

• time_series_covid19_recovered_global.csv
  Global recovered cases dataset.

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🗃️ Dataset Source

• Johns Hopkins University COVID-19 Data Repository

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🔎 Techniques Used

• Data wrangling and cleaning with pandas
• Grouping, aggregation, and data manipulation
• Descriptive statistics
• Data visualization with matplotlib and seaborn

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📊 Project Highlights

• Visualizes the worldwide spread of COVID-19 month by month
• Ranks countries by confirmed, death, and recovered cases
• Shows comparative and stacked bar charts for top affected countries
• All steps and insights are clearly explained in the notebook

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📈 Sample Visualizations